Ram C. Sharma¹, Hoan Thanh Nguyen^2,3, Saeid Gharechelou⁴, Xiulian Bai⁵, Luong Viet Nguyen⁶, Ryutaro Tateishi⁷

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¹ Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan.
² Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam.
³ Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam.
⁴ Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran.
⁵ College of Resource and Environmental Economics, Inner Mongolia University of Finance and Economics, Hohhot, China.
⁶ Space Technology Institute, Vietnam Academy of Science and Technology, Hanoi, Vietnam.
⁷ Center for Environmental Remote Sensing, Chiba University, Chiba, Japan.
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Abstract:

Derivation of more sensitive spectral features from the satellite data is immensely important for better retrieving land cover information and change monitoring, such as changes in snow covered area, forests, and barren lands as some examples from local to the global scale. The major objectives of this paper are to present the potential of water-resistant snow index (WSI) for the detection of snow cover changes in the Himalayas, extant two composite images, biophysical image composite (BIC) and forest cover composite (FCC) for the detection of changes in barren lands and forested areas respectively, and two newly designed composite images, water cover composite (WCC) and urban cover composite (UCC) for the detection of changes in water and urban areas respectively. This research implemented the image compositing technique for the detection and visualization of land cover changes (water, forest, barren, and urban) with respect to local administrative areas where a significant land cover change occurred from 2001 to 2016. A case study was also conducted in the Himalayan region to identify snow cover changes from 2001 to 2015 using the WSI. Analysis of the annual variation of the snow cover in the Himalayas indicated a decreasing trend of the snow cover. Consequently, the downstream areas are more likely to suffer from snow related hazards such as glacial outbursts, avalanches, landslides and floods. The changes in snow cover in the Himalayas may bring significant hydrophysical and livelihood changes in the downstream area including the Mekong Delta. Therefore, the countries sharing the Himalayan region should focus on adapting the severe impacts of snow cover changes. The image compositing approach presented in the research demonstrated promising performance for the detection and visualization of other land cover changes as well.

Keywords: Land Cover, Water Cover Composite, Urban Cover Composite, Snow Cover Composite, Biophysical Image Composite, Forest Cover Composite, Himalayas, MODIS, Change Detection

Cite this paper: Sharma, R. , Nguyen, H. , Gharechelou, S. , Bai, X. , Nguyen, L. and Tateishi, R. (2019) Spectral Features for the Detection of Land Cover Changes. Journal of Geoscience and Environment Protection, 7, 81-93. doi: 10.4236/gep.2019.75009.

References

[1]   Crane, R. G., & Anderson, M. R. (1984). Satellite Discrimination of Snow/Cloud Surfaces. International Journal of Remote Sensing, 5, 213-223.
https://doi.org/10.1080/01431168408948799

[2]   Deng, Y., Wu, C., Li, M., & Chen, R. (2015). RNDSI: A Ratio Normalized Difference Soil Index for Remote Sensing of Urban/Suburban Environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40-48.
https://doi.org/10.1016/j.jag.2015.02.010

[3]   Gao, B. (1996). NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58, 257-266.
https://doi.org/10.1016/S0034-4257(96)00067-3

[4]   Hall, D. K., Riggs, G. A., Salomonson, V. V., Di Girolamo, N. E., & Bayr, K. J. (2002). MODIS Snow-Cover Products. Remote Sensing of Environment, 83, 181-194.
https://doi.org/10.1016/S0034-4257(02)00095-0

[5]   Immerzeel, W. W., Droogers, P., de Jong, S. M., & Bierkens, M. F. P. (2009). Large-Scale Monitoring of Snow Cover and Runoff Simulation in Himalayan River Basins Using Remote Sensing. Remote Sensing of Environment, 113, 40-49.
https://doi.org/10.1016/j.rse.2008.08.010

[6]   Kääb, A., Berthier, E., Nuth, C., Gardelle, J., & Arnaud, Y. (2012). Contrasting Patterns of Early Twenty-First-Century Glacier Mass Change in the Himalayas. Nature, 488, 495.
https://doi.org/10.1038/nature11324

[7]   Liu, X., & Chen, B. (2000). Climatic Warming in the Tibetan Plateau during Recent Decades. International Journal of Climatology, 20, 1729-1742.
https://doi.org/10.1002/1097-0088(20001130)20:14<1729::AID-JOC556>3.0.CO;2-Y

[8]   McFeeters, S. K. (1996). The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425-1432.
https://doi.org/10.1080/01431169608948714

[9]   Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., & Dozier, J. (2009). Retrieval of Subpixel Snow Covered Area, Grain Size, and Albedo from MODIS. Remote Sensing of Environment, 113, 868-879.
https://doi.org/10.1016/j.rse.2009.01.001

[10]   Rogers, A. S., & Kearney, M. S. (2004). Reducing Signature Variability in Unmixing Coastal Marsh Thematic Mapper Scenes Using Spectral Indices. International Journal of Remote Sensing, 25, 2317-2335.
https://doi.org/10.1080/01431160310001618103

[11]   Rosenthal, W., & Dozier, J. (1996). Automated Mapping of Montane Snow Cover at Subpixel Resolution from the Landsat Thematic Mapper. Water Resources Research, 32, 115-130.
https://doi.org/10.1029/95WR02718

[12]   Rouse Jr., J., Haas, R., Schell, J., & Deering, D. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.

[13]   Salomonson, V. V., & Appel, I. (2004). Estimating Fractional Snow Cover from MODIS Using the Normalized Difference Snow Index. Remote Sensing of Environment, 89, 351-360.
https://doi.org/10.1016/j.rse.2003.10.016

[14]   Sharma, R. C., Tateishi, R., & Hara, K. (2016). A New Water-Resistant Snow Index for the Detection and Mapping of Snow Cover on a Global Scale. International Journal of Remote Sensing, 37, 2706-2723.
https://doi.org/10.1080/01431161.2016.1183832

[15]   Sharma, R. C., Tateishi, R., Hara, K., Gharechelou, S., & Iizuka, K. (2016). Global Mapping of Urban Built-Up Areas of Year 2014 by Combining MODIS Multispectral Data with VIIRS Nighttime Light Data. International Journal of Digital Earth, 9, 1004-1020.
https://doi.org/10.1080/17538947.2016.1168879

[16]   Sharma, R., Hara, K., & Tateishi, R. (2018). Developing Forest Cover Composites through a Combination of Landsat-8 Optical and Sentinel-1 SAR Data for the Visualization and Extraction of Forested Areas. Journal of Imaging, 4, 105.
https://doi.org/10.3390/jimaging4090105

[17]   Sharma, R., Tateishi, R., & Hara, K. (2016). A Biophysical Image Compositing Technique for the Global-Scale Extraction and Mapping of Barren Lands. ISPRS International Journal of Geo-Information, 5, 225.
https://doi.org/10.3390/ijgi5120225

[18]   Sharma, R., Tateishi, R., Hara, K., & Nguyen, L. (2015). Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data. Remote Sensing, 7, 13807-13841.
https://doi.org/10.3390/rs71013807

[19]   Tateishi, R., Keola, S., Uchiyama, Y., & Sharma, R. C. (2017). Development of Global-Local Datasets of Environment/Economy/Society and Their Multi-Temporal Analysis for Assessment of Sustainability. International Symposium on Remote Sensing. 17-19 May, Nagoya, Japan.
http://www.rssj.or.jp/isrs2017/wp-content/uploads/sites/3/2017/05/ISRS2017_Program_20170523_Oral.pdf

[20]   Tucker, C. J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8, 127-150.
https://doi.org/10.1016/0034-4257(79)90013-0

[21]   Zha, Y., Gao, J., & Ni, S. (2003). Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24, 583-594.
https://doi.org/10.1080/01431160304987